Transformer neural networks have revolutionized artificial intelligence by introducing an architecture built around self-attention mechanisms. This has enabled unprecedented advances in understanding sequential data, such as human languages, while also dramatically improving accuracy on nonsequential tasks like object detection. In this talk, we will explain the technical underpinnings of transformer architectures, from input data tokenization and positional encoding to the self-attention mechanism, which is the core component of these networks. We will also explore how transformers have influenced the direction of AI research and industry innovation. Finally, we will touch on trends that will likely influence how transformers evolve in the near future.